{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1623eb6a5cce817",
   "metadata": {},
   "source": [
    "# Sequence to Sequence (Seq2Seq) Model\n",
    "由于算力限制，该文件将提交至阿里云服务器运行，具体运行方法请参考阿里云文档。\n",
    "\n",
    "部分注释使用了fitten code协助生成"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d906c983343979a5",
   "metadata": {},
   "source": [
    "# import libraries"
   ]
  },
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-11T12:10:22.730591Z",
     "iopub.status.busy": "2025-03-11T12:10:22.730355Z",
     "iopub.status.idle": "2025-03-11T12:11:10.892991Z",
     "shell.execute_reply": "2025-03-11T12:11:10.892531Z",
     "shell.execute_reply.started": "2025-03-11T12:10:22.730574Z"
    },
    "ExecuteTime": {
     "end_time": "2025-03-11T12:57:30.787560Z",
     "start_time": "2025-03-11T12:57:22.272416Z"
    }
   },
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import pandas as pd\n",
    "import os\n",
    "import sys\n",
    "import time\n",
    "from tqdm.auto import tqdm\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "print(sys.version_info)\n",
    "for module in mpl, np, pd, sklearn, torch:\n",
    "    print(module.__name__, module.__version__)\n",
    "\n",
    "device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "print(device)\n",
    "\n",
    "seed = 233   # 设置随机种子，使得每次运行结果一致\n",
    "torch.manual_seed(seed)\n",
    "torch.cuda.manual_seed_all(seed)\n",
    "np.random.seed(seed)\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sys.version_info(major=3, minor=11, micro=4, releaselevel='final', serial=0)\n",
      "matplotlib 3.10.1\n",
      "numpy 2.0.2\n",
      "pandas 2.2.3\n",
      "sklearn 1.6.1\n",
      "torch 2.6.0+cu126\n",
      "cuda:0\n"
     ]
    }
   ],
   "execution_count": 1
  },
  {
   "cell_type": "markdown",
   "id": "81a0f5b6a3e63eb2",
   "metadata": {},
   "source": [
    "# Load Data"
   ]
  },
  {
   "cell_type": "code",
   "id": "2c808aa668299f13",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-11T12:11:10.894315Z",
     "iopub.status.busy": "2025-03-11T12:11:10.893832Z",
     "iopub.status.idle": "2025-03-11T12:11:11.734559Z",
     "shell.execute_reply": "2025-03-11T12:11:11.734122Z",
     "shell.execute_reply.started": "2025-03-11T12:11:10.894298Z"
    },
    "ExecuteTime": {
     "end_time": "2025-03-11T12:57:30.903447Z",
     "start_time": "2025-03-11T12:57:30.788581Z"
    }
   },
   "source": [
    "import unicodedata\n",
    "import re\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#因为西班牙语有一些是特殊字符，所以我们需要unicode转ascii，\n",
    "# unicode占字节数比ascii多，所以我们需要把unicode转ascii\n",
    "# 这样值变小了，因为unicode太大\n",
    "def unicode_to_ascii(s):\n",
    "    #NFD是转换方法，把每一个字节拆开，Mn是重音，所以去除\n",
    "    return ''.join(c for c in unicodedata.normalize('NFD', s) if unicodedata.category(c) != 'Mn')\n",
    "\n",
    "#找个样本测试一下\n",
    "# 加u代表对字符串进行unicode编码\n",
    "en_sentence = u\"May I borrow this book?\"\n",
    "sp_sentence = u\"¿Puedo tomar prestado este libro?\"\n",
    "\n",
    "print(unicode_to_ascii(en_sentence))\n",
    "print(unicode_to_ascii(sp_sentence))"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "May I borrow this book?\n",
      "¿Puedo tomar prestado este libro?\n"
     ]
    }
   ],
   "execution_count": 2
  },
  {
   "cell_type": "code",
   "id": "e18fc8c6662321db",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-11T12:11:11.735348Z",
     "iopub.status.busy": "2025-03-11T12:11:11.735074Z",
     "iopub.status.idle": "2025-03-11T12:11:11.739517Z",
     "shell.execute_reply": "2025-03-11T12:11:11.739120Z",
     "shell.execute_reply.started": "2025-03-11T12:11:11.735331Z"
    },
    "ExecuteTime": {
     "end_time": "2025-03-11T12:57:30.911181Z",
     "start_time": "2025-03-11T12:57:30.904983Z"
    }
   },
   "source": [
    "def preprocess_sentence(w):\n",
    "    \"\"\"\n",
    "    Preprocess a sentence.\n",
    "    :param w:   The sentence to preprocess.\n",
    "    :return:    The preprocessed sentence.\n",
    "    \"\"\"\n",
    "    #变为小写，去掉多余的空格，变成小写，id少一些\n",
    "    w = unicode_to_ascii(w.lower().strip())\n",
    "\n",
    "    # 在单词与跟在其后的标点符号之间插入一个空格\n",
    "    # eg: \"he is a boy.\" => \"he is a boy . \"\n",
    "    # Reference:- https://stackoverflow.com/questions/3645931/python-padding-punctuation-with-white-spaces-keeping-punctuation\n",
    "    w = re.sub(r\"([?.!,¿])\", r\" \\1 \", w)\n",
    "    #因为可能有多余空格，替换为一个空格，所以处理一下\n",
    "    w = re.sub(r'[\" \"]+', \" \", w)\n",
    "\n",
    "    # 除了 (a-z, A-Z, \".\", \"?\", \"!\", \",\")，将所有字符替换为空格，你可以保留一些标点符号\n",
    "    w = re.sub(r\"[^a-zA-Z?.!,¿]+\", \" \", w)\n",
    "\n",
    "    w = w.rstrip().strip()\n",
    "\n",
    "    return w\n",
    "\n",
    "print(preprocess_sentence(en_sentence))\n",
    "print(preprocess_sentence(sp_sentence))\n",
    "print(preprocess_sentence(sp_sentence).encode('utf-8'))  #¿是占用两个字节的"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "may i borrow this book ?\n",
      "¿ puedo tomar prestado este libro ?\n",
      "b'\\xc2\\xbf puedo tomar prestado este libro ?'\n"
     ]
    }
   ],
   "execution_count": 3
  },
  {
   "cell_type": "markdown",
   "id": "2d944f78d106754a",
   "metadata": {},
   "source": [
    "# 自定义数据集\n"
   ]
  },
  {
   "cell_type": "code",
   "id": "f5197dee26119022",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-11T12:11:11.740278Z",
     "iopub.status.busy": "2025-03-11T12:11:11.740136Z",
     "iopub.status.idle": "2025-03-11T12:11:12.458308Z",
     "shell.execute_reply": "2025-03-11T12:11:12.457823Z",
     "shell.execute_reply.started": "2025-03-11T12:11:11.740264Z"
    },
    "ExecuteTime": {
     "end_time": "2025-03-11T12:57:31.388633Z",
     "start_time": "2025-03-11T12:57:30.912385Z"
    }
   },
   "source": [
    "from pathlib import Path    #引入pathlib模块\n",
    "from torch.utils.data import Dataset, DataLoader, dataset\n",
    "\n",
    "\n",
    "class LangPairDataset(Dataset):\n",
    "    \"\"\"\n",
    "    Custom dataset for language pair.\n",
    "    \"\"\"\n",
    "    fpath = Path(r\"./data_spa_en/spa.txt\") #数据文件路径\n",
    "    cache_path = Path(r\"./.cache/lang_pair.npy\") #缓存文件路径\n",
    "    split_index = np.random.choice(a=[\"train\", \"test\"], replace=True, p=[0.9, 0.1], size=118964) #按照9:1划分训练集和测试集\n",
    "    def __init__(self, mode=\"train\", cache=False):\n",
    "        if cache or not self.cache_path.exists():#如果没有缓存，或者缓存不存在，就处理一下数据\n",
    "            self.cache_path.parent.mkdir(parents=True, exist_ok=True) #创建缓存文件夹，如果存在就忽略\n",
    "            with open(self.fpath, \"r\", encoding=\"utf8\") as file:\n",
    "                lines = file.readlines()\n",
    "                lang_pair = [[preprocess_sentence(w) for w in l.split('\\t')]  for l in lines] #处理数据，变成list((trg, src))的形式\n",
    "                trg, src = zip(*lang_pair) #分离出目标语言和源语言\n",
    "                trg=np.array(trg) #转换为numpy数组\n",
    "                src=np.array(src) #转换为numpy数组\n",
    "                np.save(self.cache_path, {\"trg\": trg, \"src\": src})  #保存为npy文件,方便下次直接读取,不用再处理\n",
    "        else:\n",
    "            lang_pair = np.load(self.cache_path, allow_pickle=True).item() #读取npy文件，allow_pickle=True允许读取字典\n",
    "            trg = lang_pair[\"trg\"]  #取出目标语言\n",
    "            src = lang_pair[\"src\"]  #取出源语言\n",
    "\n",
    "        self.trg = trg[self.split_index == mode] #按照index拿到训练集的 标签语言 --英语\n",
    "        self.src = src[self.split_index == mode] #按照index拿到训练集的源语言 --西班牙\n",
    "\n",
    "    def __getitem__(self, index):    #返回数据\n",
    "        return self.src[index], self.trg[index]\n",
    "\n",
    "    def __len__(self):    #返回数据长度\n",
    "        return len(self.src)\n",
    "\n",
    "\n",
    "train_ds = LangPairDataset(\"train\")  #训练集\n",
    "test_ds = LangPairDataset(\"test\")   #测试集"
   ],
   "outputs": [],
   "execution_count": 4
  },
  {
   "cell_type": "markdown",
   "id": "69fcc640aced2fa7",
   "metadata": {},
   "source": [
    "# Tokenize"
   ]
  },
  {
   "cell_type": "code",
   "id": "b71a27e277b37f17",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-11T12:11:12.459877Z",
     "iopub.status.busy": "2025-03-11T12:11:12.459503Z",
     "iopub.status.idle": "2025-03-11T12:11:12.997076Z",
     "shell.execute_reply": "2025-03-11T12:11:12.996588Z",
     "shell.execute_reply.started": "2025-03-11T12:11:12.459861Z"
    },
    "ExecuteTime": {
     "end_time": "2025-03-11T12:57:32.052226Z",
     "start_time": "2025-03-11T12:57:31.389636Z"
    }
   },
   "source": [
    "from collections import Counter  #引入Counter模块\n",
    "\n",
    "def get_word_idx(ds, mode=\"src\", threshold=2):\n",
    "    \"\"\"\n",
    "    Get word indices.\n",
    "    :param ds:  The dataset.\n",
    "    :param mode:    The mode of the dataset, \"src\" or \"trg\".\n",
    "    :param threshold:    The threshold of word frequency.\n",
    "    :return:    The word2idx and idx2word.\n",
    "    \"\"\"\n",
    "    #载入词表，看下词表长度，词表就像英语字典\n",
    "    word2idx = {\n",
    "        \"[PAD]\": 0,     # 填充 token\n",
    "        \"[BOS]\": 1,     # begin of sentence\n",
    "        \"[UNK]\": 2,     # 未知 token\n",
    "        \"[EOS]\": 3,     # end of sentence\n",
    "    }\n",
    "    idx2word = {value: key for key, value in word2idx.items()}  # 反向词表\n",
    "    index = len(idx2word)  # 词表长度\n",
    "    threshold = 1  # 出现次数低于此的token舍弃\n",
    "    #如果数据集有很多个G，那是用for循环的，不能' '.join\n",
    "    word_list = \" \".join([pair[0 if mode==\"src\" else 1] for pair in ds]).split()  # 取出源语言或目标语言的单词列表\n",
    "    counter = Counter(word_list) #统计词频,counter类似字典，key是单词，value是出现次数\n",
    "\n",
    "    for token, count in counter.items():    #遍历词频\n",
    "        if count >= threshold:#出现次数大于阈值的token加入词表\n",
    "            word2idx[token] = index #加入词表\n",
    "            idx2word[index] = token #加入反向词表\n",
    "            index += 1  #词表长度加1\n",
    "\n",
    "    return word2idx, idx2word    #返回词表和反向词表\n",
    "\n",
    "src_word2idx, src_idx2word = get_word_idx(train_ds, \"src\") #源语言词表  西班牙语\n",
    "trg_word2idx, trg_idx2word = get_word_idx(train_ds, \"trg\") #目标语言词表 英语"
   ],
   "outputs": [],
   "execution_count": 5
  },
  {
   "cell_type": "code",
   "id": "f463bc53b7cb9eb2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-11T12:11:12.998049Z",
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   },
   "source": [
    "# 设置Tokenizer\n",
    "class Tokenizer:\n",
    "    \"\"\"\n",
    "    Tokenizer class.\n",
    "    \"\"\"\n",
    "    def __init__(self, word2idx, idx2word, max_length=500, pad_idx=0, bos_idx=1, eos_idx=3, unk_idx=2):\n",
    "        self.word2idx = word2idx    #词表\n",
    "        self.idx2word = idx2word    #反向词表\n",
    "        self.max_length = max_length    #最大长度\n",
    "        self.pad_idx = pad_idx    #padding的index\n",
    "        self.bos_idx = bos_idx    #bos的index\n",
    "        self.eos_idx = eos_idx    #eos的index\n",
    "        self.unk_idx = unk_idx    #unk的index\n",
    "\n",
    "    def encode(self, text_list, padding_first=False, add_bos=True, add_eos=True, return_mask=False):\n",
    "        \"\"\"\n",
    "        如果padding_first == True，则padding加载前面，否则加载后面\n",
    "        return_mask: 是否返回mask(掩码），mask用于指示哪些是padding的，哪些是真实的token\n",
    "        :param text_list:    The text to encode.\n",
    "        :param padding_first:    Whether to padding load first.\n",
    "        :param add_bos:     Whether to add bos.\n",
    "        :param add_eos:     Whether to add eos.\n",
    "        :param return_mask: Whether to return mask.\n",
    "        :return:    The encoded text.\n",
    "        \"\"\"\n",
    "        max_length = min(self.max_length, add_eos + add_bos + max([len(text) for text in text_list]))    #最大长度\n",
    "        indices_list = []    #indices_list是一个list，里面是每个样本的index\n",
    "        for text in text_list:  #遍历每个样本\n",
    "            indices = [self.word2idx.get(word, self.unk_idx) for word in text[:max_length - add_bos - add_eos]] #如果词表中没有这个词，就用unk_idx代替，indices是一个list,里面是每个词的index,也就是一个样本的index\n",
    "            if add_bos:  #如果需要添加bos，就在开头添加\n",
    "                indices = [self.bos_idx] + indices\n",
    "            if add_eos:  #如果需要添加eos，就在结尾添加\n",
    "                indices = indices + [self.eos_idx]\n",
    "            if padding_first:#padding加载前面，超参可以调\n",
    "                indices = [self.pad_idx] * (max_length - len(indices)) + indices\n",
    "            else:#padding加载后面\n",
    "                indices = indices + [self.pad_idx] * (max_length - len(indices))\n",
    "            indices_list.append(indices)\n",
    "        input_ids = torch.tensor(indices_list) #转换为tensor\n",
    "        masks = (input_ids == self.pad_idx).to(dtype=torch.int64) #mask是一个和input_ids一样大小的tensor，0代表token，1代表padding，mask用于去除padding的影响\n",
    "        return input_ids if not return_mask else (input_ids, masks)\n",
    "\n",
    "\n",
    "    def decode(self, indices_list, remove_bos=True, remove_eos=True, remove_pad=True, split=False):\n",
    "        \"\"\"\n",
    "        Decode indices to text.\n",
    "        :param indices_list:    The indices to decode.\n",
    "        :param remove_bos:     Whether to remove bos.\n",
    "        :param remove_eos:     Whether to remove eos.\n",
    "        :param remove_pad:     Whether to remove pad.\n",
    "        :param split:         Whether to split the text into words.\n",
    "        :return:    The decoded text.\n",
    "        \"\"\"\n",
    "        text_list = []\n",
    "        for indices in indices_list:\n",
    "            text = []\n",
    "            for index in indices:\n",
    "                word = self.idx2word.get(index, \"[UNK]\") #如果词表中没有这个词，就用unk_idx代替\n",
    "                if remove_bos and word == \"[BOS]\":\n",
    "                    continue\n",
    "                if remove_eos and word == \"[EOS]\":#如果到达eos，就结束\n",
    "                    break\n",
    "                if remove_pad and word == \"[PAD]\":#如果到达pad，就结束\n",
    "                    break\n",
    "                text.append(word) #单词添加到列表中\n",
    "            text_list.append(\" \".join(text) if not split else text) #把列表中的单词拼接，变为一个句子\n",
    "        return text_list\n",
    "\n",
    "#两个相对于1个toknizer的好处是embedding的参数量减少\n",
    "src_tokenizer = Tokenizer(word2idx=src_word2idx, idx2word=src_idx2word) #源语言tokenizer\n",
    "trg_tokenizer = Tokenizer(word2idx=trg_word2idx, idx2word=trg_idx2word) #目标语言tokenizer\n",
    "\n",
    "raw_text = [\"hello world\".split(), \"tokenize text datas with batch\".split(), \"this is a test\".split()]\n",
    "indices,mask = trg_tokenizer.encode(raw_text, padding_first=False, add_bos=True, add_eos=True,return_mask=True)\n",
    "\n",
    "# 测试一下\n",
    "print(\"raw text\"+'-'*10)\n",
    "for raw in raw_text:\n",
    "    print(raw)\n",
    "print(\"mask\"+'-'*10)\n",
    "for m in mask:\n",
    "    print(m)\n",
    "print(\"indices\"+'-'*10)\n",
    "for index in indices:\n",
    "    print(index)\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "raw text----------\n",
      "['hello', 'world']\n",
      "['tokenize', 'text', 'datas', 'with', 'batch']\n",
      "['this', 'is', 'a', 'test']\n",
      "mask----------\n",
      "tensor([0, 0, 0, 0, 1, 1, 1])\n",
      "tensor([0, 0, 0, 0, 0, 0, 0])\n",
      "tensor([0, 0, 0, 0, 0, 0, 1])\n",
      "indices----------\n",
      "tensor([   1,   17, 3209,    3,    0,    0,    0])\n",
      "tensor([   1,    2, 3862,    2,  536,    2,    3])\n",
      "tensor([   1,  119,  235,  237, 1252,    3,    0])\n"
     ]
    }
   ],
   "execution_count": 6
  },
  {
   "cell_type": "code",
   "id": "844989a0021babf0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-11T12:11:13.613567Z",
     "iopub.status.busy": "2025-03-11T12:11:13.613368Z",
     "iopub.status.idle": "2025-03-11T12:11:13.616546Z",
     "shell.execute_reply": "2025-03-11T12:11:13.616136Z",
     "shell.execute_reply.started": "2025-03-11T12:11:13.613551Z"
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     "start_time": "2025-03-11T12:57:32.107666Z"
    }
   },
   "source": [
    "# 测试一下decode\n",
    "decode_text = trg_tokenizer.decode(indices.tolist(), remove_bos=False, remove_eos=False, remove_pad=False)\n",
    "print(\"decode text\"+'-'*10)\n",
    "for decode in decode_text:\n",
    "    print(decode)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "decode text----------\n",
      "[BOS] hello world [EOS] [PAD] [PAD] [PAD]\n",
      "[BOS] [UNK] text [UNK] with [UNK] [EOS]\n",
      "[BOS] this is a test [EOS] [PAD]\n"
     ]
    }
   ],
   "execution_count": 7
  },
  {
   "cell_type": "markdown",
   "id": "3be657c4e33a8041",
   "metadata": {},
   "source": [
    "# DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "id": "4f7794dd9be14d91",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-11T12:11:13.617354Z",
     "iopub.status.busy": "2025-03-11T12:11:13.617092Z",
     "iopub.status.idle": "2025-03-11T12:11:13.621502Z",
     "shell.execute_reply": "2025-03-11T12:11:13.621054Z",
     "shell.execute_reply.started": "2025-03-11T12:11:13.617339Z"
    },
    "ExecuteTime": {
     "end_time": "2025-03-11T12:57:32.117974Z",
     "start_time": "2025-03-11T12:57:32.111336Z"
    }
   },
   "source": [
    "def collate_fct(batch):\n",
    "    \"\"\"\n",
    "    Collate function. 该函数用于将batch内的数据处理成统一的格式，方便后续的训练\n",
    "    :param batch:    The batch to collate.\n",
    "    :return:    The collated batch.\n",
    "    \"\"\"\n",
    "    src_words = [pair[0].split() for pair in batch] #取batch内第0列进行分词，赋给src_words\n",
    "    trg_words = [pair[1].split() for pair in batch] #取batch内第1列进行分词，赋给trg_words\n",
    "\n",
    "    # [PAD] [BOS] src [EOS]\n",
    "    encoder_inputs, encoder_inputs_mask = src_tokenizer.encode(\n",
    "        src_words, padding_first=True, add_bos=True, add_eos=True, return_mask=True\n",
    "        )   \n",
    "\n",
    "    # [BOS] trg [PAD]\n",
    "    decoder_inputs = trg_tokenizer.encode(\n",
    "        trg_words, padding_first=False, add_bos=True, add_eos=False, return_mask=False,\n",
    "        )\n",
    "\n",
    "    # trg [EOS] [PAD]\n",
    "    decoder_labels, decoder_labels_mask = trg_tokenizer.encode(\n",
    "        trg_words, padding_first=False, add_bos=False, add_eos=True, return_mask=True\n",
    "        )\n",
    "\n",
    "    return {\n",
    "        \"encoder_inputs\": encoder_inputs.to(device=device),\n",
    "        \"encoder_inputs_mask\": encoder_inputs_mask.to(device=device),\n",
    "        \"decoder_inputs\": decoder_inputs.to(device=device),\n",
    "        \"decoder_labels\": decoder_labels.to(device=device),\n",
    "        \"decoder_labels_mask\": decoder_labels_mask.to(device=device), #mask用于去除padding的影响，计算loss时用\n",
    "    } #当返回的数据较多时，用dict返回比较合理\n"
   ],
   "outputs": [],
   "execution_count": 8
  },
  {
   "cell_type": "code",
   "id": "c6036b691ece3da8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-11T12:11:13.622053Z",
     "iopub.status.busy": "2025-03-11T12:11:13.621915Z",
     "iopub.status.idle": "2025-03-11T12:11:14.106656Z",
     "shell.execute_reply": "2025-03-11T12:11:14.106186Z",
     "shell.execute_reply.started": "2025-03-11T12:11:13.622040Z"
    },
    "ExecuteTime": {
     "end_time": "2025-03-11T12:57:32.307202Z",
     "start_time": "2025-03-11T12:57:32.119018Z"
    }
   },
   "source": [
    "# 测试一下\n",
    "sample_dl = DataLoader(train_ds, batch_size=2, shuffle=True, collate_fn=collate_fct)\n",
    "\n",
    "#两次执行这个代码效果不一样，因为每次执行都会shuffle\n",
    "for batch in sample_dl:\n",
    "    for key, value in batch.items():\n",
    "        print(key)\n",
    "        print(value)\n",
    "    break"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "encoder_inputs\n",
      "tensor([[   0,    0,    1,   13,   97,   14,  483,  958,  875, 6533,   15,    3],\n",
      "        [   1,  302, 8550,  867,   51, 3895,  885,  256,   88, 1222,    5,    3]],\n",
      "       device='cuda:0')\n",
      "encoder_inputs_mask\n",
      "tensor([[1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
      "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], device='cuda:0')\n",
      "decoder_inputs\n",
      "tensor([[   1,    9, 2698,  119, 2699,   10,    0,    0],\n",
      "        [   1,  429, 4518,  538, 9116,  433, 1822,    5]], device='cuda:0')\n",
      "decoder_labels\n",
      "tensor([[   9, 2698,  119, 2699,   10,    3,    0,    0],\n",
      "        [ 429, 4518,  538, 9116,  433, 1822,    5,    3]], device='cuda:0')\n",
      "decoder_labels_mask\n",
      "tensor([[0, 0, 0, 0, 0, 0, 1, 1],\n",
      "        [0, 0, 0, 0, 0, 0, 0, 0]], device='cuda:0')\n"
     ]
    }
   ],
   "execution_count": 9
  },
  {
   "cell_type": "markdown",
   "id": "3997e8ab8403bf9f",
   "metadata": {},
   "source": [
    "# Seq2Seq Model"
   ]
  },
  {
   "cell_type": "code",
   "id": "326c13c60f1de3ea",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-11T12:11:14.107418Z",
     "iopub.status.busy": "2025-03-11T12:11:14.107205Z",
     "iopub.status.idle": "2025-03-11T12:11:14.111332Z",
     "shell.execute_reply": "2025-03-11T12:11:14.110903Z",
     "shell.execute_reply.started": "2025-03-11T12:11:14.107403Z"
    },
    "ExecuteTime": {
     "end_time": "2025-03-11T12:57:32.314384Z",
     "start_time": "2025-03-11T12:57:32.308854Z"
    }
   },
   "source": [
    "class Encoder(nn.Module):\n",
    "    \"\"\"\n",
    "    Encoder class.\n",
    "    \"\"\"\n",
    "    def __init__(\n",
    "        self,\n",
    "        vocab_size, #词表大小\n",
    "        embedding_dim=256,  #embedding维度\n",
    "        hidden_dim=1024,    #隐藏层维度\n",
    "        num_layers=1,        #层数\n",
    "        ):\n",
    "        super().__init__()\n",
    "        self.embedding = nn.Embedding(vocab_size, embedding_dim)    #embedding层\n",
    "        self.gru = nn.GRU(embedding_dim, hidden_dim, num_layers=num_layers, batch_first=True)    #gru层\n",
    "\n",
    "    def forward(self, encoder_inputs):  #encoder的前向传播\n",
    "        # encoder_inputs.shape = [batch size, sequence length]\n",
    "        # bs, seq_len = encoder_inputs.shape\n",
    "        embeds = self.embedding(encoder_inputs)  #embedding层\n",
    "        # embeds.shape = [batch size, sequence length, embedding_dim]->[batch size, sequence length, hidden_dim]\n",
    "        seq_output, hidden = self.gru(embeds)        #gru层\n",
    "        # seq_output.shape = [batch size, sequence length, hidden_dim]，hidden.shape [ num_layers, batch size, hidden_dim]\n",
    "        return seq_output, hidden    #返回输出和隐藏层"
   ],
   "outputs": [],
   "execution_count": 10
  },
  {
   "cell_type": "code",
   "id": "5d62c5192865aada",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-11T12:11:14.112061Z",
     "iopub.status.busy": "2025-03-11T12:11:14.111917Z",
     "iopub.status.idle": "2025-03-11T12:11:15.696067Z",
     "shell.execute_reply": "2025-03-11T12:11:15.695577Z",
     "shell.execute_reply.started": "2025-03-11T12:11:14.112047Z"
    },
    "ExecuteTime": {
     "end_time": "2025-03-11T12:57:32.610860Z",
     "start_time": "2025-03-11T12:57:32.314896Z"
    }
   },
   "source": [
    "#把上面的Encoder写一个例子，看看输出的shape\n",
    "encoder = Encoder(vocab_size=100, embedding_dim=256, hidden_dim=1024, num_layers=4)\n",
    "encoder_inputs = torch.randint(0, 100, (2, 50))\n",
    "encoder_outputs, hidden = encoder(encoder_inputs)\n",
    "print(encoder_outputs.shape)\n",
    "print(hidden.shape)\n",
    "print(encoder_outputs[:,-1,:])\n",
    "print(hidden[-1,:,:]) #取最后一层的hidden\n",
    "# 这样是为了确保数据的正确性，防止错误"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([2, 50, 1024])\n",
      "torch.Size([4, 2, 1024])\n",
      "tensor([[-0.0147, -0.0009, -0.0042,  ..., -0.0233,  0.0162,  0.0381],\n",
      "        [ 0.0038,  0.0545,  0.0219,  ..., -0.0099, -0.0046, -0.0237]],\n",
      "       grad_fn=<SliceBackward0>)\n",
      "tensor([[-0.0147, -0.0009, -0.0042,  ..., -0.0233,  0.0162,  0.0381],\n",
      "        [ 0.0038,  0.0545,  0.0219,  ..., -0.0099, -0.0046, -0.0237]],\n",
      "       grad_fn=<SliceBackward0>)\n"
     ]
    }
   ],
   "execution_count": 11
  },
  {
   "cell_type": "markdown",
   "id": "3c2e2c9a63e54258",
   "metadata": {},
   "source": [
    "# Attention Bahdanau used in the paper"
   ]
  },
  {
   "cell_type": "code",
   "id": "db2b53f371afce72",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-11T12:11:15.696876Z",
     "iopub.status.busy": "2025-03-11T12:11:15.696620Z",
     "iopub.status.idle": "2025-03-11T12:11:15.701916Z",
     "shell.execute_reply": "2025-03-11T12:11:15.701410Z",
     "shell.execute_reply.started": "2025-03-11T12:11:15.696858Z"
    },
    "ExecuteTime": {
     "end_time": "2025-03-11T12:57:32.619403Z",
     "start_time": "2025-03-11T12:57:32.612364Z"
    }
   },
   "source": [
    "# 注意力机制在torch中也表现为一个层，这里使用BahdanauAttention，它是一种比较常用的注意力机制，也就是加权求和的注意力机制\n",
    "class BahdanauAttention(nn.Module):\n",
    "    \"\"\"\n",
    "    BahdanauAttention class.\n",
    "    \"\"\"\n",
    "    def __init__(self, hidden_dim=1024):\n",
    "        super().__init__()\n",
    "        self.Wk = nn.Linear(hidden_dim, hidden_dim) #对keys做运算，encoder的输出EO\n",
    "        self.Wq = nn.Linear(hidden_dim, hidden_dim) #对query做运算，decoder的隐藏状态\n",
    "        self.V = nn.Linear(hidden_dim, 1)\n",
    "\n",
    "    def forward(self, query, keys, values, attn_mask=None):\n",
    "        \"\"\"\n",
    "        正向传播\n",
    "        :param query: hidden state，是decoder的隐藏状态，shape = [batch size, hidden_dim]\n",
    "        :param keys: EO  [batch size, sequence length, hidden_dim]\n",
    "        :param values: EO  [batch size, sequence length, hidden_dim]\n",
    "        :param attn_mask:[batch size, sequence length],这里是encoder_inputs_mask\n",
    "        :return:\n",
    "        \"\"\"\n",
    "        # query.shape = [batch size, hidden_dim] -->通过unsqueeze(-2)增加维度 [batch size, 1, hidden_dim]\n",
    "        # keys.shape = [batch size, sequence length, hidden_dim]\n",
    "        # values.shape = [batch size, sequence length, hidden_dim]\n",
    "        scores = self.V(F.tanh(self.Wk(keys) + self.Wq(query.unsqueeze(-2)))) #unsqueeze(-2)增加维度\n",
    "        # score.shape = [batch size, sequence length, 1]\n",
    "        if attn_mask is not None: #这个mask是encoder_inputs_mask，用来mask掉padding的部分,让padding部分socres为0\n",
    "            # attn_mask is a matrix of 0/1 element,\n",
    "            # 1 means to mask logits while 0 means do nothing\n",
    "            # here we add -inf to the element while mask == 1\n",
    "            attn_mask = (attn_mask.unsqueeze(-1)) * -1e16 #在最后增加一个维度，[batch size, sequence length] --> [batch size, sequence length, 1]\n",
    "            scores += attn_mask\n",
    "        scores = F.softmax(scores, dim=-2) #对每一个词的score做softmax\n",
    "        # score.shape = [batch size, sequence length, 1]\n",
    "        context_vector = torch.mul(scores, values).sum(dim=-2) #对每一个词的score和对应的value做乘法，然后在seq_len维度上求和，得到context_vector\n",
    "        # context_vector.shape = [batch size, hidden_dim]\n",
    "        #socres用于最后的画图\n",
    "        return context_vector, scores   #返回context_vector和scores\n"
   ],
   "outputs": [],
   "execution_count": 12
  },
  {
   "cell_type": "code",
   "id": "6cc066eb856c87e8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-11T12:11:15.702543Z",
     "iopub.status.busy": "2025-03-11T12:11:15.702398Z",
     "iopub.status.idle": "2025-03-11T12:11:15.920540Z",
     "shell.execute_reply": "2025-03-11T12:11:15.920074Z",
     "shell.execute_reply.started": "2025-03-11T12:11:15.702528Z"
    },
    "ExecuteTime": {
     "end_time": "2025-03-11T12:57:32.652075Z",
     "start_time": "2025-03-11T12:57:32.620412Z"
    }
   },
   "source": [
    "# 测试一下Attention\n",
    "attention = BahdanauAttention(hidden_dim=1024)\n",
    "query = torch.randn(2, 1024) #Decoder的隐藏状态\n",
    "keys = torch.randn(2, 50, 1024) #EO\n",
    "values = torch.randn(2, 50, 1024) #EO\n",
    "attn_mask = torch.randint(0, 2, (2, 50))\n",
    "context_vector, scores = attention(query, keys, values, attn_mask)\n",
    "print(context_vector.shape)\n",
    "print(scores.shape)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([2, 1024])\n",
      "torch.Size([2, 50, 1])\n"
     ]
    }
   ],
   "execution_count": 13
  },
  {
   "cell_type": "code",
   "id": "a15c5ebed39a8754",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-11T12:11:15.922960Z",
     "iopub.status.busy": "2025-03-11T12:11:15.922731Z",
     "iopub.status.idle": "2025-03-11T12:11:15.928619Z",
     "shell.execute_reply": "2025-03-11T12:11:15.928129Z",
     "shell.execute_reply.started": "2025-03-11T12:11:15.922943Z"
    },
    "ExecuteTime": {
     "end_time": "2025-03-11T12:57:32.662806Z",
     "start_time": "2025-03-11T12:57:32.652075Z"
    }
   },
   "source": [
    "class Decoder(nn.Module):\n",
    "    \"\"\"\n",
    "    Decoder class.\n",
    "    \"\"\"\n",
    "    def __init__(\n",
    "        self,\n",
    "        vocab_size, #词表大小\n",
    "        embedding_dim=256,  #embedding维度\n",
    "        hidden_dim=1024,    #隐藏层维度\n",
    "        num_layers=1,        #层数\n",
    "        ):\n",
    "        super(Decoder, self).__init__()\n",
    "        self.embedding = nn.Embedding(vocab_size, embedding_dim)\n",
    "        self.gru = nn.GRU(embedding_dim + hidden_dim, hidden_dim, num_layers=num_layers, batch_first=True)\n",
    "        self.fc = nn.Linear(hidden_dim, vocab_size) #最后分类,词典大小是多少，就输出多少个分类\n",
    "        self.dropout = nn.Dropout(0.6)\n",
    "        self.attention = BahdanauAttention(hidden_dim) #注意力得到的context_vector\n",
    "\n",
    "    def forward(self, decoder_input, hidden, encoder_outputs, attn_mask=None):\n",
    "        #attn_mask是encoder_inputs_mask\n",
    "        # decoder_input.shape = [batch size, 1]\n",
    "        assert len(decoder_input.shape) == 2 and decoder_input.shape[-1] == 1, f\"decoder_input.shape = {decoder_input.shape} is not valid\"\n",
    "        # hidden.shape = [batch size, hidden_dim]，decoder_hidden,而第一次使用的是encoder的hidden\n",
    "        assert len(hidden.shape) == 2, f\"hidden.shape = {hidden.shape} is not valid\"\n",
    "        # encoder_outputs.shape = [batch size, sequence length, hidden_dim]\n",
    "        assert len(encoder_outputs.shape) == 3, f\"encoder_outputs.shape = {encoder_outputs.shape} is not valid\"\n",
    "        # context_vector.shape = [batch_size, hidden_dim]\n",
    "        context_vector, attention_score = self.attention(\n",
    "            query=hidden, keys=encoder_outputs, values=encoder_outputs, attn_mask=attn_mask)\n",
    "        # decoder_input.shape = [batch size, 1]\n",
    "        embeds = self.embedding(decoder_input)\n",
    "        # embeds.shape = [batch size, 1, embedding_dim]\n",
    "        # context_vector.shape = [batch size, hidden_dim] -->unsqueeze(-2)增加维度 [batch size, 1, hidden_dim]\n",
    "        embeds = torch.cat([context_vector.unsqueeze(-2), embeds], dim=-1)\n",
    "        # 新的embeds.shape = [batch size, 1, embedding_dim + hidden_dim]\n",
    "        seq_output, hidden = self.gru(embeds)\n",
    "        # seq_output.shape = [batch size, 1, hidden_dim]\n",
    "        logits = self.fc(self.dropout(seq_output))\n",
    "        # logits.shape = [batch size, 1, vocab size]，attention_score = [batch size, sequence length, 1]\n",
    "        return logits, hidden, attention_score\n",
    "\n"
   ],
   "outputs": [],
   "execution_count": 14
  },
  {
   "cell_type": "code",
   "id": "ed1e3088023f43bf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-11T12:11:15.929432Z",
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   },
   "source": [
    "# 正式引入Seq2Seq模型\n",
    "class Sequence2Sequence(nn.Module):\n",
    "    \"\"\"\n",
    "    Sequence2Sequence class.\n",
    "    \"\"\"\n",
    "    def __init__(\n",
    "        self,\n",
    "        src_vocab_size, #输入词典大小\n",
    "        trg_vocab_size, #输出词典大小\n",
    "        encoder_embedding_dim=256,\n",
    "        encoder_hidden_dim=1024, #encoder_hidden_dim和decoder_hidden_dim必须相同，是因为BahdanauAttention设计的\n",
    "        encoder_num_layers=1,    #encoder层数\n",
    "        decoder_embedding_dim=256,  \n",
    "        decoder_hidden_dim=1024,    \n",
    "        decoder_num_layers=1,    #decoder层数\n",
    "        bos_idx=1,    #开始标记\n",
    "        eos_idx=3,    #结束标记\n",
    "        max_length=512,  #最大长度\n",
    "        ):\n",
    "        super(Sequence2Sequence, self).__init__()\n",
    "        self.bos_idx = bos_idx  #开始标记\n",
    "        self.eos_idx = eos_idx  #结束标记\n",
    "        self.max_length = max_length    #最大长度\n",
    "        self.encoder = Encoder(         # encoder层数\n",
    "            src_vocab_size, #输入词典大小\n",
    "            embedding_dim=encoder_embedding_dim,    #embedding维度\n",
    "            hidden_dim=encoder_hidden_dim,    #隐藏层维度\n",
    "            num_layers=encoder_num_layers,    #层数\n",
    "            )\n",
    "        self.decoder = Decoder(         # decoder层数\n",
    "            trg_vocab_size, #输出词典大小\n",
    "            embedding_dim=decoder_embedding_dim,    #embedding维度\n",
    "            hidden_dim=decoder_hidden_dim,    #隐藏层维度\n",
    "            num_layers=decoder_num_layers,    #层数\n",
    "            )\n",
    "\n",
    "    def forward(self, *, encoder_inputs, decoder_inputs, attn_mask=None):\n",
    "        # encoding\n",
    "        encoder_outputs, hidden = self.encoder(encoder_inputs)\n",
    "        # decoding with teacher forcing\n",
    "        bs, seq_len = decoder_inputs.shape\n",
    "        logits_list = []\n",
    "        scores_list = []\n",
    "        for i in range(seq_len):#串行训练\n",
    "            # 每次迭代生成一个时间步的预测，存储在 logits_list 中，并且记录注意力分数（如果有的话）在 scores_list 中，最后将预测的logits和注意力分数拼接并返回。\n",
    "            logits, hidden, score = self.decoder(\n",
    "                decoder_inputs[:, i:i+1],\n",
    "                hidden[-1], #取最后一层的hidden，第一个时间步时，hidden是encoder的hidden，第二个时间步时，hidden是decoder的hidden\n",
    "                encoder_outputs,\n",
    "                attn_mask=attn_mask\n",
    "                )\n",
    "            logits_list.append(logits) #记录预测的logits，用于计算损失\n",
    "            scores_list.append(score) #记录注意力分数,用于画图\n",
    "\n",
    "        return torch.cat(logits_list, dim=-2), torch.cat(scores_list, dim=-1)\n",
    "\n",
    "    @torch.no_grad() #不计算梯度\n",
    "    def infer(self, encoder_input, attn_mask=None):\n",
    "        #infer用于预测\n",
    "        # encoder_input.shape = [1, sequence length],这只支持batch_size=1\n",
    "        # encoding\n",
    "        encoder_outputs, hidden = self.encoder(encoder_input)\n",
    "\n",
    "        # decoding，[[1]]\n",
    "        decoder_input = torch.Tensor([self.bos_idx]).reshape(1, 1).to(dtype=torch.int64) #shape为[1,1]，内容为开始标记\n",
    "        decoder_pred = None\n",
    "        pred_list = [] #预测序列\n",
    "        score_list = []\n",
    "        # 从开始标记 bos_idx 开始，迭代地生成序列，直到生成结束标记 eos_idx 或达到最大长度 max_length。\n",
    "        for _ in range(self.max_length):\n",
    "            logits, hidden, score = self.decoder(\n",
    "                decoder_input,\n",
    "                hidden[-1],\n",
    "                encoder_outputs,\n",
    "                attn_mask=attn_mask\n",
    "                )\n",
    "            # using greedy search,logits shape = [1, 1, vocab size]\n",
    "            decoder_pred = logits.argmax(dim=-1)\n",
    "            decoder_input = decoder_pred\n",
    "            pred_list.append(decoder_pred.reshape(-1).item()) #decoder_pred从(1,1)变为（1）标量\n",
    "            score_list.append(score) #记录注意力分数,用于画图\n",
    "\n",
    "            # stop at eos token\n",
    "            if decoder_pred == self.eos_idx:\n",
    "                break\n",
    "\n",
    "        # return\n",
    "        return pred_list, torch.cat(score_list, dim=-1)\n",
    "\n"
   ],
   "outputs": [],
   "execution_count": 15
  },
  {
   "cell_type": "code",
   "id": "4980add9e7e205e6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-11T12:11:15.937514Z",
     "iopub.status.busy": "2025-03-11T12:11:15.937235Z",
     "iopub.status.idle": "2025-03-11T12:11:16.419683Z",
     "shell.execute_reply": "2025-03-11T12:11:16.419200Z",
     "shell.execute_reply.started": "2025-03-11T12:11:15.937499Z"
    },
    "ExecuteTime": {
     "end_time": "2025-03-11T12:57:33.323758Z",
     "start_time": "2025-03-11T12:57:32.676968Z"
    }
   },
   "source": [
    "# 测试一下Seq2Seq模型\n",
    "model = Sequence2Sequence(src_vocab_size=len(src_word2idx), trg_vocab_size=len(trg_word2idx))\n",
    "#做model的前向传播，看看输出的shape\n",
    "encoder_inputs = torch.randint(0, 100, (2, 50))\n",
    "decoder_inputs = torch.randint(0, 100, (2, 50))\n",
    "attn_mask = torch.randint(0, 2, (2, 50))\n",
    "logits, scores = model(encoder_inputs=encoder_inputs, decoder_inputs=decoder_inputs, attn_mask=attn_mask)\n",
    "print(logits.shape)\n",
    "print(scores.shape)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([2, 50, 12460])\n",
      "torch.Size([2, 50, 50])\n"
     ]
    }
   ],
   "execution_count": 16
  },
  {
   "cell_type": "code",
   "id": "1d6d31fbdbc40de",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-11T12:11:16.420512Z",
     "iopub.status.busy": "2025-03-11T12:11:16.420349Z",
     "iopub.status.idle": "2025-03-11T12:11:16.423695Z",
     "shell.execute_reply": "2025-03-11T12:11:16.423240Z",
     "shell.execute_reply.started": "2025-03-11T12:11:16.420495Z"
    },
    "ExecuteTime": {
     "end_time": "2025-03-11T12:57:33.330739Z",
     "start_time": "2025-03-11T12:57:33.326005Z"
    }
   },
   "source": [
    "#计算一下model的总参数量\n",
    "def count_parameters(model):\n",
    "    return sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
    "\n",
    "print(f\"The model has {count_parameters(model):,} trainable parameters\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model has 35,165,101 trainable parameters\n"
     ]
    }
   ],
   "execution_count": 17
  },
  {
   "cell_type": "code",
   "id": "ae5b83292aa1cd98",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-11T12:11:16.424347Z",
     "iopub.status.busy": "2025-03-11T12:11:16.424171Z",
     "iopub.status.idle": "2025-03-11T12:11:16.816774Z",
     "shell.execute_reply": "2025-03-11T12:11:16.816135Z",
     "shell.execute_reply.started": "2025-03-11T12:11:16.424333Z"
    },
    "ExecuteTime": {
     "end_time": "2025-03-11T12:57:33.736383Z",
     "start_time": "2025-03-11T12:57:33.331784Z"
    }
   },
   "source": [
    "#初始化一个4层的GRU的model\n",
    "model2=Sequence2Sequence(src_vocab_size=len(src_word2idx), trg_vocab_size=len(trg_word2idx), encoder_num_layers=4, decoder_num_layers=4)\n",
    "print(f\"The model has {count_parameters(model2):,} trainable parameters\")\n",
    "\n",
    "# 多层GRU表现更不好，因此不适用多层，只用一层GRU"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model has 72,950,701 trainable parameters\n"
     ]
    }
   ],
   "execution_count": 18
  },
  {
   "cell_type": "markdown",
   "id": "2ae7ecc6cf3ef992",
   "metadata": {},
   "source": [
    "# 训练Seq2Seq模型\n",
    "很明显由于参数过多，后面的代码放到阿里云"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5332d82f0033d009",
   "metadata": {},
   "source": [
    "## 损失函数设计"
   ]
  },
  {
   "cell_type": "code",
   "id": "5337362cb64e057",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-11T12:11:16.817708Z",
     "iopub.status.busy": "2025-03-11T12:11:16.817327Z",
     "iopub.status.idle": "2025-03-11T12:11:16.821078Z",
     "shell.execute_reply": "2025-03-11T12:11:16.820692Z",
     "shell.execute_reply.started": "2025-03-11T12:11:16.817691Z"
    },
    "ExecuteTime": {
     "end_time": "2025-03-11T12:57:33.743371Z",
     "start_time": "2025-03-11T12:57:33.737787Z"
    }
   },
   "source": [
    "# 采用交叉熵损失函数，并设计mask机制，使得padding部分的损失不计入损失函数的计算。\n",
    "def cross_entropy_with_padding(logits, labels, padding_mask=None):\n",
    "    # logits.shape = [batch size, sequence length, num of classes]\n",
    "    # labels.shape = [batch size, sequence length]\n",
    "    # padding_mask.shape = [batch size, sequence length] decoder_labels_mask\n",
    "    bs, seq_len, nc = logits.shape\n",
    "    loss = F.cross_entropy(logits.reshape(bs * seq_len, nc), labels.reshape(-1), reduce=False) #reduce=False表示不对batch求平均\n",
    "    if padding_mask is None:#如果没有padding_mask，就直接求平均\n",
    "        loss = loss.mean()\n",
    "    else:\n",
    "        # 如果提供了 padding_mask，则将padding填充部分的损失去除后计算有效损失的均值。首先，通过将 padding_mask reshape 成一维张量，并取 1 减去得到填充掩码。这样填充部分的掩码值变为 1，非填充部分变为 0。将损失张量与填充掩码相乘，这样填充部分的损失就会变为 0。然后，计算非填充部分的损失和（sum）以及非填充部分的掩码数量（sum）作为有效损失的均值计算。(因为上面我们设计的mask的token是0，所以这里是1-padding_mask)\n",
    "        padding_mask = 1 - padding_mask.reshape(-1) #将padding_mask reshape成一维张量，mask部分为0，非mask部分为1\n",
    "        loss = torch.mul(loss, padding_mask).sum() / padding_mask.sum()\n",
    "\n",
    "    return loss\n"
   ],
   "outputs": [],
   "execution_count": 19
  },
  {
   "cell_type": "markdown",
   "id": "dfa663b3a58d32e9",
   "metadata": {},
   "source": [
    "## callback设计"
   ]
  },
  {
   "cell_type": "code",
   "id": "c2d1fc4c6ba6b59",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-11T12:11:16.821875Z",
     "iopub.status.busy": "2025-03-11T12:11:16.821532Z",
     "iopub.status.idle": "2025-03-11T12:11:17.538012Z",
     "shell.execute_reply": "2025-03-11T12:11:17.537553Z",
     "shell.execute_reply.started": "2025-03-11T12:11:16.821859Z"
    },
    "ExecuteTime": {
     "end_time": "2025-03-11T12:57:38.974404Z",
     "start_time": "2025-03-11T12:57:33.744900Z"
    }
   },
   "source": [
    "from torch.utils.tensorboard import SummaryWriter\n",
    "\n",
    "\n",
    "class TensorBoardCallback:\n",
    "    def __init__(self, log_dir, flush_secs=10):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            log_dir (str): dir to write log.\n",
    "            flush_secs (int, optional): write to dsk each flush_secs seconds. Defaults to 10.\n",
    "        \"\"\"\n",
    "        self.writer = SummaryWriter(log_dir=log_dir, flush_secs=flush_secs)\n",
    "\n",
    "    def draw_model(self, model, input_shape):\n",
    "        self.writer.add_graph(model, input_to_model=torch.randn(input_shape))\n",
    "\n",
    "    def add_loss_scalars(self, step, loss, val_loss):\n",
    "        self.writer.add_scalars(\n",
    "            main_tag=\"training/loss\",\n",
    "            tag_scalar_dict={\"loss\": loss, \"val_loss\": val_loss},\n",
    "            global_step=step,\n",
    "            )\n",
    "\n",
    "    def add_acc_scalars(self, step, acc, val_acc):\n",
    "        self.writer.add_scalars(\n",
    "            main_tag=\"training/accuracy\",\n",
    "            tag_scalar_dict={\"accuracy\": acc, \"val_accuracy\": val_acc},\n",
    "            global_step=step,\n",
    "        )\n",
    "\n",
    "    def add_lr_scalars(self, step, learning_rate):\n",
    "        self.writer.add_scalars(\n",
    "            main_tag=\"training/learning_rate\",\n",
    "            tag_scalar_dict={\"learning_rate\": learning_rate},\n",
    "            global_step=step,\n",
    "\n",
    "        )\n",
    "\n",
    "    def __call__(self, step, **kwargs):\n",
    "        # add loss\n",
    "        loss = kwargs.pop(\"loss\", None)\n",
    "        val_loss = kwargs.pop(\"val_loss\", None)\n",
    "        if loss is not None and val_loss is not None:\n",
    "            self.add_loss_scalars(step, loss, val_loss)\n",
    "        # add acc\n",
    "        acc = kwargs.pop(\"acc\", None)\n",
    "        val_acc = kwargs.pop(\"val_acc\", None)\n",
    "        if acc is not None and val_acc is not None:\n",
    "            self.add_acc_scalars(step, acc, val_acc)\n",
    "        # add lr\n",
    "        learning_rate = kwargs.pop(\"lr\", None)\n",
    "        if learning_rate is not None:\n",
    "            self.add_lr_scalars(step, learning_rate)\n"
   ],
   "outputs": [],
   "execution_count": 20
  },
  {
   "cell_type": "code",
   "id": "a984521bae87a2eb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-11T12:11:17.538951Z",
     "iopub.status.busy": "2025-03-11T12:11:17.538563Z",
     "iopub.status.idle": "2025-03-11T12:11:17.543069Z",
     "shell.execute_reply": "2025-03-11T12:11:17.542675Z",
     "shell.execute_reply.started": "2025-03-11T12:11:17.538933Z"
    },
    "ExecuteTime": {
     "end_time": "2025-03-11T12:57:38.982786Z",
     "start_time": "2025-03-11T12:57:38.975426Z"
    }
   },
   "source": [
    "# 保存最优模型\n",
    "class SaveCheckpointsCallback:\n",
    "    def __init__(self, save_dir, save_step=5000, save_best_only=True):\n",
    "        \"\"\"\n",
    "        Save checkpoints each save_epoch epoch.\n",
    "        We save checkpoint by epoch in this implementation.\n",
    "        Usually, training scripts with pytorch evaluating model and save checkpoint by step.\n",
    "\n",
    "        Args:\n",
    "            save_dir (str): dir to save checkpoint\n",
    "            save_epoch (int, optional): the frequency to save checkpoint. Defaults to 1.\n",
    "            save_best_only (bool, optional): If True, only save the best model or save each model at every epoch.\n",
    "        \"\"\"\n",
    "        self.save_dir = save_dir\n",
    "        self.save_step = save_step\n",
    "        self.save_best_only = save_best_only\n",
    "        self.best_metrics = - np.inf\n",
    "\n",
    "        # mkdir\n",
    "        if not os.path.exists(self.save_dir):\n",
    "            os.mkdir(self.save_dir)\n",
    "\n",
    "    def __call__(self, step, state_dict, metric=None):\n",
    "        if step % self.save_step > 0:\n",
    "            return\n",
    "\n",
    "        if self.save_best_only:\n",
    "            assert metric is not None\n",
    "            if metric >= self.best_metrics:\n",
    "                # save checkpoints\n",
    "                torch.save(state_dict, os.path.join(self.save_dir, \"best.ckpt\"))\n",
    "                # update best metrics\n",
    "                self.best_metrics = metric\n",
    "        else:\n",
    "            torch.save(state_dict, os.path.join(self.save_dir, f\"{step}.ckpt\"))\n",
    "\n"
   ],
   "outputs": [],
   "execution_count": 21
  },
  {
   "cell_type": "markdown",
   "id": "5c8531dfc7161759",
   "metadata": {},
   "source": [
    "## 定义早停机制"
   ]
  },
  {
   "cell_type": "code",
   "id": "34b159f84d62e92d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-11T12:11:17.543827Z",
     "iopub.status.busy": "2025-03-11T12:11:17.543505Z",
     "iopub.status.idle": "2025-03-11T12:11:17.547115Z",
     "shell.execute_reply": "2025-03-11T12:11:17.546738Z",
     "shell.execute_reply.started": "2025-03-11T12:11:17.543798Z"
    },
    "ExecuteTime": {
     "end_time": "2025-03-11T12:57:38.988704Z",
     "start_time": "2025-03-11T12:57:38.983794Z"
    }
   },
   "source": [
    "class EarlyStopCallback:\n",
    "    def __init__(self, patience=5, min_delta=0.001):\n",
    "        \"\"\"\n",
    "\n",
    "        Args:\n",
    "            patience (int, optional): Number of epochs with no improvement after which training will be stopped.. Defaults to 5.\n",
    "            min_delta (float, optional): Minimum change in the monitored quantity to qualify as an improvement, i.e. an absolute\n",
    "                change of less than min_delta, will count as no improvement. Defaults to 0.01.\n",
    "        \"\"\"\n",
    "        self.patience = patience\n",
    "        self.min_delta = min_delta\n",
    "        self.best_metric = - np.inf\n",
    "        self.counter = 0\n",
    "\n",
    "    def __call__(self, metric):\n",
    "        if metric >= self.best_metric + self.min_delta:\n",
    "            # update best metric\n",
    "            self.best_metric = metric\n",
    "            # reset counter\n",
    "            self.counter = 0\n",
    "        else:\n",
    "            self.counter += 1\n",
    "\n",
    "    @property\n",
    "    def early_stop(self):\n",
    "        return self.counter >= self.patience\n"
   ],
   "outputs": [],
   "execution_count": 22
  },
  {
   "cell_type": "markdown",
   "id": "737d5cdc21d14e17",
   "metadata": {},
   "source": [
    "## 训练和评估"
   ]
  },
  {
   "cell_type": "code",
   "id": "bedfd3aa952bdf5a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-11T12:11:17.547909Z",
     "iopub.status.busy": "2025-03-11T12:11:17.547587Z",
     "iopub.status.idle": "2025-03-11T12:11:17.551121Z",
     "shell.execute_reply": "2025-03-11T12:11:17.550695Z",
     "shell.execute_reply.started": "2025-03-11T12:11:17.547893Z"
    },
    "ExecuteTime": {
     "end_time": "2025-03-11T12:57:38.995390Z",
     "start_time": "2025-03-11T12:57:38.989714Z"
    }
   },
   "source": [
    "# 定义训练函数\n",
    "@torch.no_grad()\n",
    "def evaluating(model, dataloader, loss_fct):\n",
    "    loss_list = []\n",
    "    for batch in dataloader:\n",
    "        encoder_inputs = batch[\"encoder_inputs\"]\n",
    "        encoder_inputs_mask = batch[\"encoder_inputs_mask\"]\n",
    "        decoder_inputs = batch[\"decoder_inputs\"]\n",
    "        decoder_labels = batch[\"decoder_labels\"]\n",
    "        decoder_labels_mask = batch[\"decoder_labels_mask\"]\n",
    "\n",
    "        # 前向计算\n",
    "        logits, _ = model(\n",
    "            encoder_inputs=encoder_inputs,\n",
    "            decoder_inputs=decoder_inputs,\n",
    "            attn_mask=encoder_inputs_mask\n",
    "            ) #model就是seq2seq模型\n",
    "        loss = loss_fct(logits, decoder_labels, padding_mask=decoder_labels_mask)         # 验证集损失\n",
    "        loss_list.append(loss.cpu().item())\n",
    "\n",
    "    return np.mean(loss_list)\n"
   ],
   "outputs": [],
   "execution_count": 23
  },
  {
   "cell_type": "code",
   "id": "990543a960a5dbe5",
   "metadata": {
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-03-11T12:11:17.551866Z",
     "iopub.status.busy": "2025-03-11T12:11:17.551697Z",
     "iopub.status.idle": "2025-03-11T12:11:28.669168Z",
     "shell.execute_reply": "2025-03-11T12:11:28.668684Z",
     "shell.execute_reply.started": "2025-03-11T12:11:17.551852Z"
    },
    "tags": [],
    "ExecuteTime": {
     "end_time": "2025-03-11T12:57:41.660006Z",
     "start_time": "2025-03-11T12:57:38.996398Z"
    }
   },
   "source": [
    "# 训练\n",
    "def training(\n",
    "    model,\n",
    "    train_loader,\n",
    "    val_loader,\n",
    "    epoch,\n",
    "    loss_fct,\n",
    "    optimizer,\n",
    "    tensorboard_callback=None,\n",
    "    save_ckpt_callback=None,\n",
    "    early_stop_callback=None,\n",
    "    eval_step=500,\n",
    "    ):\n",
    "    record_dict = {\n",
    "        \"train\": [],\n",
    "        \"val\": []\n",
    "    }\n",
    "\n",
    "    global_step = 1\n",
    "    model.train() # 切换到训练模式\n",
    "    with tqdm(total=epoch * len(train_loader)) as pbar:\n",
    "        for epoch_id in range(epoch):\n",
    "            # training\n",
    "            for batch in train_loader:\n",
    "                encoder_inputs = batch[\"encoder_inputs\"]\n",
    "                encoder_inputs_mask = batch[\"encoder_inputs_mask\"]\n",
    "                decoder_inputs = batch[\"decoder_inputs\"]\n",
    "                decoder_labels = batch[\"decoder_labels\"]\n",
    "                decoder_labels_mask = batch[\"decoder_labels_mask\"]\n",
    "\n",
    "                # 梯度清空\n",
    "                optimizer.zero_grad()\n",
    "\n",
    "                # 前向计算\n",
    "                logits, _ = model(\n",
    "                    encoder_inputs=encoder_inputs,\n",
    "                    decoder_inputs=decoder_inputs,\n",
    "                    attn_mask=encoder_inputs_mask\n",
    "                    )\n",
    "                loss = loss_fct(logits, decoder_labels, padding_mask=decoder_labels_mask)\n",
    "\n",
    "                # 梯度回传\n",
    "                loss.backward()\n",
    "\n",
    "                # 调整优化器，包括学习率的变动等\n",
    "                optimizer.step()\n",
    "\n",
    "                loss = loss.cpu().item()\n",
    "                # record\n",
    "                record_dict[\"train\"].append({\n",
    "                    \"loss\": loss, \"step\": global_step\n",
    "                })\n",
    "\n",
    "                # evaluating\n",
    "                if global_step % eval_step == 0:\n",
    "                    model.eval() # 切换到验证模式\n",
    "                    val_loss = evaluating(model, val_loader, loss_fct)\n",
    "                    record_dict[\"val\"].append({\n",
    "                        \"loss\": val_loss, \"step\": global_step\n",
    "                    })\n",
    "                    model.train() # 切换到训练模式\n",
    "\n",
    "                    # 1. 使用 tensorboard 可视化\n",
    "                    if tensorboard_callback is not None:\n",
    "                        tensorboard_callback(\n",
    "                            global_step,\n",
    "                            loss=loss, val_loss=val_loss,\n",
    "                            lr=optimizer.param_groups[0][\"lr\"],\n",
    "                            )\n",
    "\n",
    "                    # 2. 保存模型权重 save model checkpoint\n",
    "                    if save_ckpt_callback is not None:\n",
    "                        save_ckpt_callback(global_step, model.state_dict(), metric=-val_loss)\n",
    "\n",
    "                    # 3. 早停 Early Stop\n",
    "                    if early_stop_callback is not None:\n",
    "                        early_stop_callback(-val_loss)\n",
    "                        if early_stop_callback.early_stop:\n",
    "                            print(f\"Early stop at epoch {epoch_id} / global_step {global_step}\")\n",
    "                            return record_dict\n",
    "\n",
    "                # udate step\n",
    "                global_step += 1\n",
    "                pbar.update(1)\n",
    "            pbar.set_postfix({\"epoch\": epoch_id, \"loss\": loss, \"val_loss\": val_loss}) # 更新进度条\n",
    "\n",
    "    return record_dict\n",
    "\n",
    "\n",
    "epoch = 20\n",
    "batch_size = 64\n",
    "\n",
    "model = Sequence2Sequence(src_vocab_size=len(src_word2idx), trg_vocab_size=len(trg_word2idx))\n",
    "train_dl = DataLoader(train_ds, batch_size=batch_size, shuffle=True, collate_fn=collate_fct)\n",
    "test_dl = DataLoader(test_ds, batch_size=batch_size, shuffle=False, collate_fn=collate_fct)\n",
    "\n",
    "# 1. 定义损失函数 采用交叉熵损失\n",
    "loss_fct = cross_entropy_with_padding\n",
    "# 2. 定义优化器 采用 adam\n",
    "# Optimizers specified in the torch.optim package\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
    "\n",
    "# 1. tensorboard 可视化\n",
    "if not os.path.exists(\"runs\"):\n",
    "    os.mkdir(\"runs\")\n",
    "exp_name = \"translate-seq2seq\"\n",
    "tensorboard_callback = TensorBoardCallback(f\"runs/{exp_name}\")\n",
    "# tensorboard_callback.draw_model(model, [1, MAX_LENGTH])\n",
    "# 2. save best\n",
    "if not os.path.exists(\"checkpoints\"):\n",
    "    os.makedirs(\"checkpoints\")\n",
    "save_ckpt_callback = SaveCheckpointsCallback(\n",
    "    f\"{exp_name}\", save_step=200, save_best_only=True)\n",
    "# 3. early stop\n",
    "early_stop_callback = EarlyStopCallback(patience=5)\n",
    "\n",
    "model = model.to(device)\n",
    "\n"
   ],
   "outputs": [],
   "execution_count": 24
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5dfd350cbe63ccf3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-11T02:52:03.473111Z",
     "start_time": "2025-03-11T02:50:43.764770Z"
    },
    "execution": {
     "iopub.execute_input": "2025-03-11T12:11:28.670172Z",
     "iopub.status.busy": "2025-03-11T12:11:28.669748Z",
     "iopub.status.idle": "2025-03-11T12:21:54.331983Z",
     "shell.execute_reply": "2025-03-11T12:21:54.331379Z",
     "shell.execute_reply.started": "2025-03-11T12:11:28.670156Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/33480 [00:00<?, ?it/s]/usr/local/lib/python3.10/site-packages/torch/nn/_reduction.py:51: UserWarning: size_average and reduce args will be deprecated, please use reduction='none' instead.\n",
      "  warnings.warn(warning.format(ret))\n",
      " 27%|██▋       | 9199/33480 [10:25<27:31, 14.70it/s, epoch=4, loss=1.6, val_loss=1.25]    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stop at epoch 5 / global_step 9200\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# 训练模型\n",
    "record = training(\n",
    "    model,\n",
    "    train_dl,\n",
    "    test_dl,\n",
    "    epoch,\n",
    "    loss_fct,\n",
    "    optimizer,\n",
    "    tensorboard_callback=None,\n",
    "    save_ckpt_callback=save_ckpt_callback,\n",
    "    early_stop_callback=early_stop_callback,\n",
    "    eval_step=200\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ffde8e76df3cfdaa",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-11T06:30:48.793754Z",
     "start_time": "2025-03-11T06:30:48.449624Z"
    },
    "execution": {
     "iopub.execute_input": "2025-03-11T12:21:54.333412Z",
     "iopub.status.busy": "2025-03-11T12:21:54.332939Z",
     "iopub.status.idle": "2025-03-11T12:21:54.417985Z",
     "shell.execute_reply": "2025-03-11T12:21:54.417231Z",
     "shell.execute_reply.started": "2025-03-11T12:21:54.333381Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画图\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot([i[\"step\"] for i in record[\"train\"]], [i[\"loss\"] for i in record[\"train\"]], label=\"train\")\n",
    "plt.plot([i[\"step\"] for i in record[\"val\"]], [i[\"loss\"] for i in record[\"val\"]], label=\"val\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d2b9a52913cf4a7",
   "metadata": {},
   "source": [
    "# 推理和预测"
   ]
  },
  {
   "cell_type": "code",
   "id": "74d43d1991c5e34a",
   "metadata": {
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-03-11T12:46:06.234159Z",
     "iopub.status.busy": "2025-03-11T12:46:06.233798Z",
     "iopub.status.idle": "2025-03-11T12:46:06.478341Z",
     "shell.execute_reply": "2025-03-11T12:46:06.477453Z",
     "shell.execute_reply.started": "2025-03-11T12:46:06.234139Z"
    },
    "tags": [],
    "ExecuteTime": {
     "end_time": "2025-03-11T12:58:12.880985Z",
     "start_time": "2025-03-11T12:58:12.603504Z"
    }
   },
   "source": [
    "# load checkpoints,如何上线\n",
    "model = Sequence2Sequence(src_vocab_size=len(src_word2idx), trg_vocab_size=len(trg_word2idx))\n",
    "model.load_state_dict(torch.load(f\"./checkpoints/translate-seq2seq/best.ckpt\", weights_only=True,map_location=\"cpu\"))\n",
    "\n",
    "class Translator:\n",
    "    def __init__(self, model, src_tokenizer, trg_tokenizer):\n",
    "        self.model = model\n",
    "        self.model.eval() # 切换到验证模式\n",
    "        self.src_tokenizer = src_tokenizer\n",
    "        self.trg_tokenizer = trg_tokenizer\n",
    "\n",
    "    def draw_attention_map(self, scores, src_words_list, trg_words_list):\n",
    "        \"\"\"\n",
    "        绘制注意力热力图\n",
    "        Args:\n",
    "            - scores (numpy.ndarray): shape = [source sequence length, target sequence length]\n",
    "            - src_words_list (list): 源语言的单词列表\n",
    "            - trg_words_list (list): 目标语言的单词列表\n",
    "        \"\"\"\n",
    "        import matplotlib.pyplot as plt\n",
    "        \n",
    "        # 绘制热力图\n",
    "        plt.figure(figsize=(10, 8))  # 设置图尺寸\n",
    "        im = plt.matshow(scores.T, cmap='coolwarm',fignum=1)\n",
    "        plt.colorbar(im, fraction=0.046, pad=0.04)  # 添加颜色条并调整其位置\n",
    "    \n",
    "        ax = plt.gca()\n",
    "    \n",
    "        # 设置单元格中的文本\n",
    "        for i in range(scores.shape[1]):  # 遍历列数（目标单词）\n",
    "            for j in range(scores.shape[0]):  # 遍历行数（源单词）\n",
    "                text = ax.text(\n",
    "                    j, i, f\"{scores[j, i]:.2f}\",  # 获取相应的注意力值\n",
    "                    ha=\"center\", va=\"center\", color=\"black\"\n",
    "                )\n",
    "    \n",
    "        # 调整图例\n",
    "        ax.set_xticks(range(len(src_words_list)))\n",
    "        ax.set_yticks(range(len(trg_words_list)))\n",
    "        ax.set_xticklabels(src_words_list, fontsize=12, rotation=45)  # 源单词显示，并旋转一定角度\n",
    "        ax.set_yticklabels(trg_words_list, fontsize=12)\n",
    "    \n",
    "        # 调整布局，避免标签被裁剪\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    def __call__(self, sentence):\n",
    "        sentence = preprocess_sentence(sentence) # 预处理句子，标点符号处理等\n",
    "        encoder_input, attn_mask = self.src_tokenizer.encode(\n",
    "            [sentence.split()],\n",
    "            padding_first=True,\n",
    "            add_bos=True,\n",
    "            add_eos=True,\n",
    "            return_mask=True,\n",
    "            ) # 对输入进行编码，并返回encode_piadding_mask\n",
    "        encoder_input = torch.Tensor(encoder_input).to(dtype=torch.int64) # 转换成tensor\n",
    "\n",
    "        preds, scores = model.infer(encoder_input=encoder_input, attn_mask=attn_mask) #预测\n",
    "\n",
    "        trg_sentence = self.trg_tokenizer.decode([preds], split=True, remove_eos=False)[0] #通过tokenizer转换成文字\n",
    "\n",
    "        src_decoded = self.src_tokenizer.decode(\n",
    "            encoder_input.tolist(),\n",
    "            split=True,\n",
    "            remove_bos=False,\n",
    "            remove_eos=False\n",
    "            )[0] #对输入编码id进行解码，转换成文字,为了画图\n",
    "\n",
    "        self.draw_attention_map(\n",
    "            scores.squeeze(0).numpy(),\n",
    "            src_decoded, # 注意力图的源句子\n",
    "            trg_sentence # 注意力图的目标句子\n",
    "            )\n",
    "        return \" \".join(trg_sentence[:-1])\n",
    "\n",
    "\n"
   ],
   "outputs": [],
   "execution_count": 29
  },
  {
   "cell_type": "code",
   "id": "38d21a2d6ba215d3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-11T12:46:06.877427Z",
     "iopub.status.busy": "2025-03-11T12:46:06.877123Z",
     "iopub.status.idle": "2025-03-11T12:46:07.057073Z",
     "shell.execute_reply": "2025-03-11T12:46:07.056577Z",
     "shell.execute_reply.started": "2025-03-11T12:46:06.877405Z"
    },
    "tags": [],
    "ExecuteTime": {
     "end_time": "2025-03-11T12:58:13.801864Z",
     "start_time": "2025-03-11T12:58:13.469258Z"
    }
   },
   "source": [
    "# 实例化翻译器\n",
    "translator = Translator(model.cpu(), src_tokenizer, trg_tokenizer)\n",
    "translator(u'hace mucho frio aqui .')"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\glue\\AppData\\Local\\Temp\\ipykernel_55820\\1971765313.py:44: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'it s very cold here .'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 30
  },
  {
   "cell_type": "code",
   "id": "72ad55361d4ad438",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-11T12:46:07.683168Z",
     "iopub.status.busy": "2025-03-11T12:46:07.682855Z",
     "iopub.status.idle": "2025-03-11T12:46:07.854396Z",
     "shell.execute_reply": "2025-03-11T12:46:07.853812Z",
     "shell.execute_reply.started": "2025-03-11T12:46:07.683148Z"
    },
    "tags": [],
    "ExecuteTime": {
     "end_time": "2025-03-11T12:58:15.040766Z",
     "start_time": "2025-03-11T12:58:14.720085Z"
    }
   },
   "source": [
    "# 使用几个例子测试\n",
    "translator(u'Hoy es un buen día.')"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\glue\\AppData\\Local\\Temp\\ipykernel_55820\\1971765313.py:44: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'today is a good day .'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 31
  },
  {
   "cell_type": "code",
   "id": "f3992be9-de8f-43a5-8b9e-854180c45309",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-11T12:46:08.172646Z",
     "iopub.status.busy": "2025-03-11T12:46:08.172330Z",
     "iopub.status.idle": "2025-03-11T12:46:08.652617Z",
     "shell.execute_reply": "2025-03-11T12:46:08.652184Z",
     "shell.execute_reply.started": "2025-03-11T12:46:08.172625Z"
    },
    "tags": [],
    "ExecuteTime": {
     "end_time": "2025-03-11T12:58:16.684375Z",
     "start_time": "2025-03-11T12:58:16.175978Z"
    }
   },
   "source": [
    "# 使用几个例子测试\n",
    "translator(u'No tienes nada que hacer cuando estás libre.')"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\glue\\AppData\\Local\\Temp\\ipykernel_55820\\1971765313.py:44: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'you have nothing to make any when you re free .'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 32
  },
  {
   "cell_type": "code",
   "id": "1f6e0bb3-809c-4a10-a80e-60a87a18f886",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-11T12:46:24.544933Z",
     "iopub.status.busy": "2025-03-11T12:46:24.544547Z",
     "iopub.status.idle": "2025-03-11T12:46:24.750306Z",
     "shell.execute_reply": "2025-03-11T12:46:24.749745Z",
     "shell.execute_reply.started": "2025-03-11T12:46:24.544884Z"
    },
    "tags": [],
    "ExecuteTime": {
     "end_time": "2025-03-11T12:58:18.307001Z",
     "start_time": "2025-03-11T12:58:17.980282Z"
    }
   },
   "source": [
    "translator(u'¿¿ cuándo vas a estudiar?.')"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\glue\\AppData\\Local\\Temp\\ipykernel_55820\\1971765313.py:44: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'when are you going to study ?'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 33
  },
  {
   "cell_type": "code",
   "id": "a5abf9461ca2c697",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-11T12:46:12.460601Z",
     "iopub.status.busy": "2025-03-11T12:46:12.460280Z",
     "iopub.status.idle": "2025-03-11T12:46:12.586524Z",
     "shell.execute_reply": "2025-03-11T12:46:12.585924Z",
     "shell.execute_reply.started": "2025-03-11T12:46:12.460582Z"
    },
    "tags": [],
    "ExecuteTime": {
     "end_time": "2025-03-11T12:58:19.452053Z",
     "start_time": "2025-03-11T12:58:19.197022Z"
    }
   },
   "source": [
    "translator(u've ahora mismo .')"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\glue\\AppData\\Local\\Temp\\ipykernel_55820\\1971765313.py:44: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'go now .'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 34
  },
  {
   "cell_type": "markdown",
   "id": "880846414e46a207",
   "metadata": {},
   "source": [
    "# 计算BLEU1分数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2dcaf0b50008beb3",
   "metadata": {
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-03-11T12:28:33.657444Z",
     "iopub.status.busy": "2025-03-11T12:28:33.657129Z",
     "iopub.status.idle": "2025-03-11T12:34:44.285577Z",
     "shell.execute_reply": "2025-03-11T12:34:44.284932Z",
     "shell.execute_reply.started": "2025-03-11T12:28:33.657424Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_368/3848423051.py:3: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  model.load_state_dict(torch.load(f\"./translate-seq2seq/best.ckpt\", map_location=\"cpu\"))\n",
      "Evaluating BLEU score:   0%|          | 0/11887 [00:00<?, ?it/s]/usr/local/lib/python3.10/site-packages/nltk/translate/bleu_score.py:577: UserWarning: \n",
      "The hypothesis contains 0 counts of 3-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n",
      "/usr/local/lib/python3.10/site-packages/nltk/translate/bleu_score.py:577: UserWarning: \n",
      "The hypothesis contains 0 counts of 4-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n",
      "/usr/local/lib/python3.10/site-packages/nltk/translate/bleu_score.py:577: UserWarning: \n",
      "The hypothesis contains 0 counts of 2-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n",
      "Evaluating BLEU score: 100%|██████████| 11887/11887 [06:10<00:00, 32.12it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average BLEU score: 0.6989\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# 计算 BLEU 分数\n",
    "model = Sequence2Sequence(len(src_word2idx), len(trg_word2idx))\n",
    "model.load_state_dict(torch.load(f\"./translate-seq2seq/best.ckpt\", map_location=\"cpu\"))\n",
    "\n",
    "\n",
    "class Translator:\n",
    "    def __init__(self, model, src_tokenizer, trg_tokenizer):\n",
    "        self.model = model\n",
    "        self.model.eval()  # 切换到验证模式\n",
    "        self.src_tokenizer = src_tokenizer\n",
    "        self.trg_tokenizer = trg_tokenizer\n",
    "\n",
    "    def __call__(self, sentence):\n",
    "        sentence = preprocess_sentence(sentence)  # 预处理句子，标点符号处理等\n",
    "        encoder_input, attn_mask = self.src_tokenizer.encode(\n",
    "            [sentence.split()],\n",
    "            padding_first=True,\n",
    "            add_bos=True,\n",
    "            add_eos=True,\n",
    "            return_mask=True,\n",
    "        )  # 对输入进行编码，并返回encode_piadding_mask\n",
    "        encoder_input = torch.Tensor(encoder_input).to(dtype=torch.int64)  # 转换成tensor\n",
    "\n",
    "        preds, scores = model.infer(encoder_input=encoder_input, attn_mask=attn_mask)  #预测\n",
    "\n",
    "        trg_sentence = self.trg_tokenizer.decode([preds], split=True, remove_eos=False)[0]  #通过tokenizer转换成文字\n",
    "\n",
    "        return \" \".join(trg_sentence[:-1])\n",
    "\n",
    "\n",
    "from nltk.translate.bleu_score import sentence_bleu\n",
    "\n",
    "from tqdm import tqdm\n",
    "\n",
    "\n",
    "def evaluate_bleu_on_test_set(test_data, translator):\n",
    "    total_bleu = 0.0\n",
    "    num_samples = len(test_data)\n",
    "\n",
    "    # 用 tqdm 包装 test_data 显示进度条\n",
    "    for src_sentence, ref_translations in tqdm(test_data, desc=\"Evaluating BLEU score\"):\n",
    "        candidate = translator(src_sentence).split()\n",
    "        # 将 references 转换为列表的列表格式\n",
    "        references = [ref.split() for ref in ref_translations] if isinstance(ref_translations, list) else [\n",
    "            ref_translations.split()]\n",
    "\n",
    "        bleu = sentence_bleu(\n",
    "            references,\n",
    "            candidate,\n",
    "            weights=(1, 0, 0, 0)\n",
    "        )\n",
    "        total_bleu += bleu\n",
    "\n",
    "    return total_bleu / num_samples\n",
    "\n",
    "\n",
    "translator = Translator(model.cpu(), src_tokenizer, trg_tokenizer)\n",
    "avg_bleu = evaluate_bleu_on_test_set(test_ds, translator)\n",
    "\n",
    "print(f\"Average BLEU score: {avg_bleu:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "dfc6d4dec6ca2dbe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-11T12:34:44.287417Z",
     "iopub.status.busy": "2025-03-11T12:34:44.286772Z",
     "iopub.status.idle": "2025-03-11T12:40:53.859669Z",
     "shell.execute_reply": "2025-03-11T12:40:53.859168Z",
     "shell.execute_reply.started": "2025-03-11T12:34:44.287386Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluating BLEU score: 100%|██████████| 11887/11887 [06:09<00:00, 32.16it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average BLEU-4 score: 0.3481\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# 计算 BLEU4 分数\n",
    "def evaluate_bleu_on_test_set_4(test_data, translator):\n",
    "    total_bleu = 0.0\n",
    "    num_samples = len(test_data)\n",
    "\n",
    "    # 用 tqdm 包装 test_data 显示进度条\n",
    "    for src_sentence, ref_translations in tqdm(test_data, desc=\"Evaluating BLEU score\"):\n",
    "        candidate = translator(src_sentence).split()\n",
    "        # 将 references 转换为列表的列表格式\n",
    "        references = [ref.split() for ref in ref_translations] if isinstance(ref_translations, list) else [\n",
    "            ref_translations.split()]\n",
    "\n",
    "        bleu = sentence_bleu(\n",
    "            references,\n",
    "            candidate,\n",
    "            weights=(0.25, 0.25, 0.25, 0.25)\n",
    "        )\n",
    "        total_bleu += bleu\n",
    "\n",
    "    return total_bleu / num_samples\n",
    "\n",
    "\n",
    "translator = Translator(model.cpu(), src_tokenizer, trg_tokenizer)\n",
    "avg_bleu_4 = evaluate_bleu_on_test_set_4(test_ds, translator)\n",
    "\n",
    "print(f\"Average BLEU-4 score: {avg_bleu_4:.4f}\")"
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "可知评分汇总为：<br>\n",
    "| 评分 | 值 |<br>\n",
    "| --- | --- |<br>\n",
    "| BLEU-1 | 0.6989 |<br>\n",
    "| BLEU-4 | 0.3481 |<br>\n",
    "效果较好，可以考虑上线。"
   ],
   "id": "82d8343c3893ff13"
  }
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